NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images. (arXiv:2209.09498v1 [cs.CV])

In recent years, deep neural network-based restoration methods have achieved
state-of-the-art results in various image deblurring tasks. However, one major
drawback of deep learning-based deblurring networks is that large amounts of
blurry-clean image pairs are required for training to achieve good performance.
Moreover, deep networks often fail to perform well when the blurry images and
the blur kernels during testing are very different from the ones used during
training. This happens mainly because of the overfitting of the network
parameters …

arxiv gap image images